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Pairwise Learning for Neural Link Prediction
[article]
2022
arXiv
pre-print
In this paper, we aim at providing an effective Pairwise Learning Neural Link Prediction (PLNLP) framework. The framework treats link prediction as a pairwise learning to rank problem and consists of four main components, i.e., neighborhood encoder, link predictor, negative sampler and objective function. The framework is flexible that any generic graph neural convolution or link prediction specific neural architecture could be employed as neighborhood encoder. For link predictor, we design
arXiv:2112.02936v6
fatcat:q3rzbj33h5aohgncppbjrl3vda